Statistical optimization of process variables for the large-scale production of Metarhizium anisopliae conidiospores in solid-state fermentation
Optimization of conidial production was achieved by response surface methodology (RSM), a powerful mathematical approach widely applied in the optimization of fermentation process, using the three substrates; rice, barley and sorghum at variable pH, moisture content and yeast extract concentrations....
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Veröffentlicht in: | Bioresource technology 2008-04, Vol.99 (6), p.1530-1537 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Optimization of conidial production was achieved by response surface methodology (RSM), a powerful mathematical approach widely applied in the optimization of fermentation process, using the three substrates; rice, barley and sorghum at variable pH, moisture content and yeast extract concentrations. These three factors were found to be important, affecting
Metarhizium anisopliae spore production. A 2
3 full factorial central composite design and RSM were applied to determine the optimal concentration of each variable. A second-order polynomial was determined by the multiple regression analysis of the experimental data. Moisture content of 75.68% for sorghum, 73.21% for barley and 22.34% for rice produced optimal results. Maximal conidial yield was recorded for rice at a pH of 7.01; at 7.06 for sorghum and at 6.76 for barley. |
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ISSN: | 0960-8524 1873-2976 |
DOI: | 10.1016/j.biortech.2007.04.031 |